Abstract
A Boolean dynamical system integrating the main signaling pathways involved in cancer is constructed based on the currently known protein-protein interaction network. This system exhibits stationary protein activation patterns – attractors – dependent on the cell's microenvironment. These dynamical attractors were determined through simulations and their stabilities against mutations were tested. In a higher hierarchical level, it was possible to group the network attractors into distinct cell phenotypes and determine driver mutations that promote phenotypic transitions. We find that driver nodes are not necessarily central in the network topology, but at least they are direct regulators of central components towards which converge or through which crosstalk distinct cancer signaling pathways. The predicted drivers are in agreement with those pointed out by diverse census of cancer genes recently performed for several human cancers. Furthermore, our results demonstrate that cell phenotypes can evolve towards full malignancy through distinct sequences of accumulated mutations. In particular, the network model supports routes of carcinogenesis known for some tumor types. Finally, the Boolean network model is employed to evaluate the outcome of molecularly targeted cancer therapies. The major find is that monotherapies were additive in their effects and that the association of targeted drugs is necessary for cancer eradication.
Highlights
Cancer is a genetic disease derived, with few exceptions, from mutations on single somatic cells that disregard the normal control of proliferation, invade adjacent normal tissues, and give rise to secondary tumors on sites different from its primary origin [1]
In the absence of an intact mutation free (DNA) damage repair pathway, in which Atm and Atr play central roles, our results indicate that network attractors become more prone to structural changes or, in biological terms, exhibit increased genomic instability
We constructed a Boolean dynamical system integrating the main cancer signaling pathways in a simplified network. The dynamics of this network is controlled by attractors associated to apoptotic, proliferative and quiescent phenotypes that qualitatively reproduce the behaviors of a normal cell under diverse microenvironmental conditions
Summary
Cancer is a genetic disease derived, with few exceptions, from mutations on single somatic cells that disregard the normal control of proliferation, invade adjacent normal tissues, and give rise to secondary tumors (metastasis) on sites different from its primary origin [1]. The tumor growth is intrinsically multiscale in nature It involves phenomena occurring over a variety of spatial scales ranging from tissue (for instance, invasion and angiogenesis) to molecular length scales (for example, mutations and gene silencing), while the timescales vary from seconds for signaling to years for tumor doubling times. Information flows from the finer to coarser scales, but between any pair of scales [6]
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